Note
Go to the end to download the full example code
Exclude images from transform#
In this example we show how the kwargs include
and exclude
can be
used to apply a transform to only some of the images within a subject.
Downloading http://www.bic.mni.mcgill.ca/~vfonov/nihpd/obj1/nihpd_asym_04.5-08.5_nifti.zip to /home/user/.cache/torchio/nihpd_asym_04.5-08.5_nifti/nihpd_asym_04.5-08.5_nifti.zip
0it [00:00, ?it/s]
0%| | 0/58169474 [00:02<?, ?it/s]
0%| | 16384/58169474 [00:02<06:31, 148709.34it/s]
0%| | 40960/58169474 [00:02<05:01, 192529.97it/s]
0%| | 90112/58169474 [00:02<03:07, 310132.07it/s]
0%| | 188416/58169474 [00:02<01:48, 535157.88it/s]
1%| | 360448/58169474 [00:02<01:02, 923119.66it/s]
1%| | 704512/58169474 [00:02<00:34, 1684465.66it/s]
2%|▏ | 1368064/58169474 [00:02<00:17, 3207011.69it/s]
3%|▎ | 1982464/58169474 [00:02<00:13, 4086666.22it/s]
4%|▍ | 2605056/58169474 [00:03<00:19, 2899526.39it/s]
5%|▌ | 3031040/58169474 [00:03<00:20, 2713964.14it/s]
10%|▉ | 5750784/58169474 [00:03<00:07, 7338757.18it/s]
11%|█▏ | 6643712/58169474 [00:03<00:07, 6793533.32it/s]
13%|█▎ | 7430144/58169474 [00:03<00:07, 6467467.77it/s]
14%|█▍ | 8151040/58169474 [00:04<00:07, 6331994.82it/s]
15%|█▌ | 8839168/58169474 [00:04<00:08, 6137622.37it/s]
16%|█▋ | 9543680/58169474 [00:04<00:07, 6346482.69it/s]
18%|█▊ | 10207232/58169474 [00:04<00:08, 5985268.66it/s]
19%|█▊ | 10829824/58169474 [00:04<00:08, 5882866.44it/s]
20%|█▉ | 11526144/58169474 [00:04<00:07, 6066498.90it/s]
21%|██ | 12148736/58169474 [00:04<00:07, 5986706.73it/s]
22%|██▏ | 12754944/58169474 [00:04<00:07, 5965603.83it/s]
23%|██▎ | 13426688/58169474 [00:04<00:07, 6117552.18it/s]
24%|██▍ | 14073856/58169474 [00:05<00:07, 6217185.64it/s]
25%|██▌ | 14737408/58169474 [00:05<00:06, 6313096.96it/s]
27%|██▋ | 15425536/58169474 [00:05<00:06, 6477767.30it/s]
28%|██▊ | 16080896/58169474 [00:05<00:06, 6464184.86it/s]
29%|██▉ | 16760832/58169474 [00:05<00:06, 6497712.86it/s]
30%|██▉ | 17440768/58169474 [00:05<00:06, 6580261.07it/s]
31%|███ | 18104320/58169474 [00:05<00:06, 6580744.96it/s]
32%|███▏ | 18776064/58169474 [00:05<00:05, 6613237.85it/s]
33%|███▎ | 19456000/58169474 [00:05<00:05, 6649001.88it/s]
35%|███▍ | 20144128/58169474 [00:05<00:05, 6714028.94it/s]
36%|███▌ | 20815872/58169474 [00:06<00:05, 6703463.00it/s]
37%|███▋ | 21487616/58169474 [00:06<00:05, 6696286.29it/s]
38%|███▊ | 22159360/58169474 [00:06<00:05, 6679072.87it/s]
39%|███▉ | 22847488/58169474 [00:06<00:05, 6731675.05it/s]
41%|████ | 23560192/58169474 [00:06<00:05, 6836435.08it/s]
42%|████▏ | 24248320/58169474 [00:06<00:05, 6472333.32it/s]
43%|████▎ | 24985600/58169474 [00:06<00:04, 6649474.39it/s]
44%|████▍ | 25657344/58169474 [00:06<00:04, 6537592.50it/s]
45%|████▌ | 26329088/58169474 [00:06<00:04, 6544758.44it/s]
47%|████▋ | 27058176/58169474 [00:06<00:04, 6758993.81it/s]
48%|████▊ | 27738112/58169474 [00:07<00:04, 6770403.27it/s]
49%|████▉ | 28450816/58169474 [00:07<00:04, 6865181.04it/s]
50%|█████ | 29147136/58169474 [00:07<00:04, 6888885.83it/s]
51%|█████▏ | 29843456/58169474 [00:07<00:04, 6856628.60it/s]
53%|█████▎ | 30564352/58169474 [00:07<00:03, 6958366.65it/s]
54%|█████▍ | 31268864/58169474 [00:07<00:03, 6958654.86it/s]
55%|█████▍ | 31989760/58169474 [00:07<00:03, 7032125.60it/s]
56%|█████▌ | 32694272/58169474 [00:07<00:03, 6869851.20it/s]
57%|█████▋ | 33415168/58169474 [00:07<00:03, 6964187.37it/s]
59%|█████▊ | 34119680/58169474 [00:08<00:06, 3977355.11it/s]
60%|█████▉ | 34668544/58169474 [00:08<00:07, 3199339.25it/s]
61%|██████ | 35504128/58169474 [00:08<00:05, 4079854.70it/s]
62%|██████▏ | 36069376/58169474 [00:08<00:05, 4130712.39it/s]
63%|██████▎ | 36593664/58169474 [00:08<00:05, 4314789.92it/s]
64%|██████▍ | 37134336/58169474 [00:08<00:04, 4562811.35it/s]
65%|██████▍ | 37707776/58169474 [00:09<00:04, 4818970.81it/s]
66%|██████▌ | 38264832/58169474 [00:09<00:03, 4999004.47it/s]
67%|██████▋ | 38830080/58169474 [00:09<00:03, 5149728.12it/s]
68%|██████▊ | 39378944/58169474 [00:09<00:03, 5151647.13it/s]
69%|██████▊ | 39919616/58169474 [00:09<00:03, 5197364.32it/s]
70%|██████▉ | 40534016/58169474 [00:09<00:03, 5405299.44it/s]
71%|███████ | 41132032/58169474 [00:09<00:03, 5543858.91it/s]
72%|███████▏ | 41697280/58169474 [00:09<00:02, 5514790.21it/s]
73%|███████▎ | 42254336/58169474 [00:09<00:02, 5422164.46it/s]
74%|███████▎ | 42876928/58169474 [00:09<00:02, 5614621.42it/s]
75%|███████▍ | 43442176/58169474 [00:10<00:02, 5587491.23it/s]
76%|███████▌ | 44007424/58169474 [00:10<00:02, 5481031.42it/s]
77%|███████▋ | 44654592/58169474 [00:10<00:02, 5758667.87it/s]
78%|███████▊ | 45268992/58169474 [00:10<00:02, 5838332.03it/s]
79%|███████▉ | 45858816/58169474 [00:10<00:02, 5747734.50it/s]
80%|███████▉ | 46440448/58169474 [00:10<00:02, 5764738.27it/s]
81%|████████ | 47071232/58169474 [00:10<00:01, 5908477.14it/s]
82%|████████▏ | 47669248/58169474 [00:10<00:01, 5737853.16it/s]
83%|████████▎ | 48250880/58169474 [00:10<00:01, 5751655.96it/s]
84%|████████▍ | 48840704/58169474 [00:11<00:01, 5783791.03it/s]
85%|████████▍ | 49422336/58169474 [00:11<00:01, 5538253.41it/s]
86%|████████▌ | 50069504/58169474 [00:11<00:01, 5803445.40it/s]
87%|████████▋ | 50659328/58169474 [00:11<00:01, 5818860.34it/s]
88%|████████▊ | 51257344/58169474 [00:11<00:01, 5842790.98it/s]
89%|████████▉ | 51863552/58169474 [00:11<00:01, 5839985.10it/s]
90%|█████████ | 52518912/58169474 [00:11<00:00, 6000478.20it/s]
91%|█████████▏| 53125120/58169474 [00:11<00:00, 5973870.06it/s]
92%|█████████▏| 53723136/58169474 [00:11<00:00, 5932396.26it/s]
93%|█████████▎| 54345728/58169474 [00:11<00:00, 5929362.04it/s]
94%|█████████▍| 54943744/58169474 [00:12<00:00, 5862692.71it/s]
95%|█████████▌| 55533568/58169474 [00:12<00:00, 5866055.22it/s]
97%|█████████▋| 56139776/58169474 [00:12<00:00, 5911343.19it/s]
98%|█████████▊| 56737792/58169474 [00:12<00:00, 5851072.56it/s]
99%|█████████▊| 57401344/58169474 [00:12<00:00, 6043333.55it/s]
100%|█████████▉| 58032128/58169474 [00:12<00:00, 6118414.59it/s]
58171392it [00:12, 4625473.27it/s]
import torch
import torchio as tio
torch.manual_seed(0)
subject = tio.datasets.Pediatric(years=(4.5, 8.5))
subject.plot()
transform = tio.Compose([
tio.RandomAffine(degrees=(20, 30), exclude='t1'),
tio.RandomBlur(std=(3, 4), include='t2'),
])
transformed = transform(subject)
transformed.plot()
Total running time of the script: (0 minutes 21.768 seconds)